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Chronic Fatigue Syndrome > Publications > Molecular Epidemology Publications >

Odds ratio based multifactor-dimensionality reduction method for detecting gene-gene interactions.

Chung Y, Lee SY, Elston RC, Park T.
Bioinformatics 2007; 71-76.

Summary

Following the 2005 Cold Spring Harbor - Banbury Center CFS Computational Challenge (C3) Workshop, CDC provided data sets from the Wichita in-hospital clinical study to Duke University for use in the Sixth International Conference for the Critical Assessment of Microarray Data Analysis (CAMDA 2006).  Duke University founded CAMDA to provide a forum to critically assess different techniques used in microarray data mining.  CAMDA’s aim is to establish the state-of-the-art in microarray data mining and to identify progress and highlight the direction for future effort.  CAMDA utilizes a community-wide experiment approach, letting the scientific community analyze the same standard data sets.  Researchers worldwide are invited to take the CAMDA challenge and those whose results are accepted are invited to present a 25 minute oral presentation.  The 2006 CAMDA was the first to use a single common challenge data set, which contained all clinical, gene expression, SNP, and proteomics data from the Wichita clinical study.

To date 10 peer reviewed publications have resulted from the CAMDA challenge.  This analysis and publication was a collaborative effort between The Departments of Statistics, Seoul National University, Applied Mathematics Sejong University, Korea and Epidemiology & biostatistics, Case Western Reserve Univesity, cleveland, Ohio.  The revised a mathematical method called multifactor dimensionality reduction to identify combinations of genes associated with complex multifactorial diseases and used the Wichita in-hospital genetics data to illustrate their new method.

Abstract

Motivation: The identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases is a challenging task in genetic association studies. The multifactor dimensionality reduction (MDR) method has been proposed and implemented by Ritchie et al. (2001) to identify the combinations of multilocus genotypes and discrete environmental factors that are associated with a particular disease. However, the original MDR method classifies the combination of multilocus genotypes into high risk and low-risk groups in an ad hoc manner based on a simple comparison of the ratios of the number of cases and controls. Hence, the MDR approach is prone to false positive and negative errors when the ratio of the number of cases and controls in a combination of genotypes is similar to that in the entire data, or when both the number of cases and controls is small. Hence, we propose the odds ratio based multifactor dimensionality reduction (OR MDR) method that uses the odds ratio as a new quantitative measure of disease risk.

Results: While the original MDR method provides a simple binary measures of risk, the OR MDR method provides not only the odds ratio as a quantitative measure of risk but also the ordering of the multilocus combinations from the highest risk to lowest risk groups. Furthermore, the OR MDR method provides a confidence interval for the odds ratio for each multilocus combination, which is extremely informative in judging its importance as a risk factor. The proposed OR MDR method is illustrated using the dataset obtained from the CDC Chronic Fatigue Syndrome Research Group.

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Page last modified on October 24, 2007

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